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• W2022603596 abstract "Chemical liabilities, such as adverse effects and toxicity, have a major impact on today's drug discovery process. In silico prediction of chemical liabilities is an important approach which can reduce costs and animal testing by complementing or replacing in vitro and in vivo liability models. There is a lack of integrated, extensible decision support systems for chemical liability assessment which run quickly and have easily interpretable results. Here we present a method which integrates similarity searches, structural alerts, and QSAR models which all are available from the Bioclipse workbench. Emphasis has been placed on interpretation of results, and substructures which are important for predictions are highlighted in the original chemical structures. This allows for interactively changing chemical structures with instant visual feedback and can be used for hypothesis testing of single chemical structures as well as compound collections. The system has a clear separation between methods and data, and the extensible architecture enables straightforward extension via addition of more plugins (such as new data sets and computational models). We demonstrate our method on three important safety end points: mutagenicity, carcinogenicity, and aryl hydrocarbon receptor (AhR) activation. Bioclipse and the decision support implementation are free, open source, and available from http://www.bioclipse.net/decision-support ." @default.
• W2022603596 created "2016-06-24" @default.
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• W2022603596 date "2011-08-05" @default.
• W2022603596 modified "2023-09-26" @default.
• W2022603596 title "Integrated Decision Support for Assessing Chemical Liabilities" @default.
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• W2022603596 doi "https://doi.org/10.1021/ci200242c" @default.
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